Assessing the Impact of Lightning Data Assimilation in the WRF Model

Vargas, Vanderlei; Ferreira, Rute Costa; Pinto, Osmar; Herdies, Dirceu Luis

doi:10.3390/atmos15070826

i

Assessing the Impact of Lightning Data Assimilation in the WRF Model

2024
By Vargas, Vanderlei ; Ferreira, Rute Costa ; Pinto, Osmar ; ...

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Journal Title:

Atmosphere
Personal Author:

Vargas, Vanderlei ; Ferreira, Rute Costa ; Pinto, Osmar ; Herdies, Dirceu Luis
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CIRA (Cooperative Institute for Research in the Atmosphere) ; GSL (Global Systems Laboratory)
Description:

Recent advancements in computational technologies have enhanced the importance of meteorological modeling, driven by an increased reliance on weather-dependent systems. This research implemented a lightning data assimilation technique to improve short-term weather forecasts in South America, potentially refining initialization methods used in meteorological operation centers. The main goal was to implement and enhance a data assimilation algorithm integrating lightning data into the WRF model, assessing its impact on forecast accuracy. Focusing on southern Brazil, a region with extensive observational infrastructure and frequent meteorological activity, this research utilized several data sources: precipitation data from the National Institute of Meteorology (INMET), lightning data from the Brazilian Lightning Detection Network (BrasilDAT), GOES-16 satellite images, synoptic weather charts from the National Institute for Space Research (INPE), and initial conditions from the GFS model. Employing the WRF-ARW model version 3.9.1.1 and WRFDA system version 3.9.1 with 3DVAR methodology, the study conducted three experimental setups during two meteorological events to evaluate the assimilation algorithm. These included a control (CTRL) without assimilation, a lightning data assimilation (LIGHT), and an adaptive humidity threshold assimilation (ALIGHT). Results showed that the lightning data assimilation system enhanced forecasts for large-scale systems, especially with humidity threshold adjustments. While it improved squall line timing and positioning, it had mixed effects when convection was thermally driven. The lightning data assimilation methodology represents a significant contribution to the field, indicating that using such alternative data can markedly improve short-term forecasts, benefiting various societal sectors.
Source:

Atmosphere, 15(7), 826
DOI:

https://doi.org/10.3390/atmos15070826
ISSN:

2073-4433
Format:

pdf
Publisher:

MDPI AG
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
Main Document Checksum:

urn:sha-512:f34841d01f8705d5bd3a49b365021cd914a76d3e7c17db070e6eec5e1fd76ccf1a03b97264a35850af71b963f29e78a54e335eb8e93961bab8af34c2394c53a1
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https://repository.library.noaa.gov/view/noaa/69600/noaa_69600_DS1.pdf
File Type:

[PDF - 22.94 MB ]

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